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The use cases that drive XNAT developmentDan MarcusJune 24, 2012
4 Driving Use Cases
• Institutional repositories• Multi-center studies• Data sharing• Clinical research• (There are others – individual labs,
small animal imaging, etc.)
Institutional repositories
• Organizational Characteristics:–Multiple studies (i.e. protocols, projects)–Multiple investigators–Multiple modalities–Multiple user types (PIs, RAs, students,
techs, external collaborators, etc.)– Common imaging protocols– Common data elements
Institutional repositories
• Technical Characteristics:– Common computing resources (e.g. data
storage, computing grid)– Common data resources (e.g PACS,
clinical database).– Common authentication/authorization
resources (e.g. active directory, university login system)
Institutional repositories
• XNAT Capabilities– Project-based security and navigation– DICOM C-Store– Custom routing rules– Pipeline management– LDAP authentication
Institutional repositories
• XNAT Gaps– Pushing protocols to scanners.
Institutional repositories
• Example:– Central Neuroimaging Data Archive
(CNDA) (“The Original XNAT”)• 831 Projects, 17026 Subjects, 23351
Imaging Sessions, 240 PIs.• Direct connectivity to all research scanners• Direct access to department data storage• Direct connectivity to department
computing resources.
Institutional repositories
• Central Neuroimaging Data Archive (CNDA) (“The Original XNAT”)– 831 Projects, 17026 Subjects, 23351
Imaging Sessions, 240 PIs.– Direct connectivity to all research
scanners– Direct access to department data
storage– Direct connectivity to department
computing resources.
Institutional repositories
• CNDA Data Sources– Center for Clinical Imaging Research
(CCIR)– East Building MRI Facility– External Investigators (multi-center
trials, collaborators)
Data import from East Building1. Investigators opt in.2. Investigators create their own
projects.3. Whoever runs scan manually enters
project ID in Study Comments4. Whoever runs scan sends scan to
CNDA destination5. Whoever screws up calls helpdesk to
locate scan.
Data import from CCIR
1. Betsy Thomas, head coordinator, manages creation of new protocols, assigns protocol #.
2. Betsy creates project in CNDA using protocol # as project ID.
3. Betsy notifies PI re: CNDA project.4. Scanner tech creates project specific protocol
on scanner. CCIR# is written to DICOM header.5. Scanner tech sends all scans to CNDA
immediately after acquisition.6. Automated scripts sent to scanner techs to
“close the loop”.
Funding models
• Per project (or subject or scan) fee• Departmental support• Center funding• Large scale projects• Industry support
Spinning out mini repositories • When to do it?• How to do it?
Multicenter studies
• Organizational Characteristics:– One primary PI–Many site PIs– One data coordinating center–Many staff, many roles–Multiple data sources, one unified data
set–Multiple data access policies
Multicenter trials
• Technical Characteristics:– Remote uploads over unreliable
networks– Central image database– Separate clinical database– Common protocol (with variations)– Common image analysis (with
variations)–Many ways to screw up
Multicenter trials
• XNAT Capabilities– Between-project sharing– Image acquisition validation– Programmatic API– Protocol validation– Visualization– 21 CFR Part 11 compliance*
* Requires additional commercial modules from Radiologics
Multicenter trials
• XNAT Gaps– Notification service– Rule engine– Site query service
Multicenter trials
• Example:– Dominantly Inherited Alzheimer Network (DIAN)
• Longitudinal study• 12 sites• 269 participants• Extensive protocol (MRI, 2x PET, tissue, clinical
battery, etc)• Clinical coordinating center (and clinical DB) at
Alzheimer’s Disease Cooperative Study (ADCS), UCSD• MRI QC at Mayo clinic• PET QC at U of Michigan• Radiology eads by Wash U diagnostic group
DIAN Dataflow
Coordinating Data
• Images uploaded via upload applet.• Psychometrics uploaded via custom
form.• PET QC completed through online forms
(Easy breezy).• Radiology reads completed through
online viewer and forms (Easy breezy).• Processed image data through automated
pipelines (Tough but worthwhile).
Coordinating Data
• MR QC imported through ETL process– Data extracted from Mayo DB into
spreadsheet.– Spreadsheet transformed to XNAT XML.– XML loaded to CNDA by NRG scripts.– Havoc ensues.
Coordinating Data
• Clinical data imported through ETL process– Data extracted from EDC by NRG via
programmatic interface.– Data transformed to XML by NRG
scripts.– XML loaded to CNDA by NRG scripts.– Havoc DOESN’T ensue.
Coordinating Data
• What’s the difference?–Mayo uses patient name field in DICOM
which might not match the database.–MRI QC values trigger actions (queries,
uploads) so changes cause lots of confusion.
–Wash U controls clinical data transfers, so if things get weird are aware and can resolve.